code: def run_example():
      logger.info("Running relation extraction example......")
      computed = ComputedRelationExtraction()
      ref_dict = ReferentDictRelationExtraction(
          ref_dict=str(LIBRARY_ROOT / "datasets" / "coref.dict1.tsv")
      )
      vo = VerboceanRelationExtraction(
          vo_file=str(
              LIBRARY_ROOT
              / "datasets"
              / "verbocean.unrefined.2004-05-20.txt"
          )
      )
      wiki = WikipediaRelationExtraction()
      wn = WordnetRelationExtraction()
      embed = WordEmbeddingRelationExtraction(
          method=EmbeddingMethod.ELMO
      )
  
      mention_x1 = MentionDataLight(
          "IBM",
          mention_context="IBM manufactures and markets computer hardware, middleware and software",
      )
      mention_y1 = MentionDataLight(
          "International Business Machines",
          mention_context="International Business Machines Corporation is an "
          "American multinational information technology company",
      )
  
      computed_relations = computed.extract_all_relations(
          mention_x1, mention_y1
      )
      ref_dict_relations = ref_dict.extract_all_relations(
          mention_x1, mention_y1
      )
      vo_relations = vo.extract_all_relations(mention_x1, mention_y1)
      wiki_relations = wiki.extract_all_relations(
          mention_x1, mention_y1
      )
      embed_relations = embed.extract_all_relations(
          mention_x1, mention_y1
      )
      wn_relaions = wn.extract_all_relations(mention_x1, mention_y1)
  
      if RelationType.NO_RELATION_FOUND in computed_relations:
          logger.info("No Computed relation found")
      else:
          logger.info(
              "Found Computed relations-%s",
              str(list(computed_relations)),
          )
  
      if RelationType.NO_RELATION_FOUND in ref_dict_relations:
          logger.info("No Referent-Dict relation found")
      else:
          logger.info(
              "Found Referent-Dict relations-%s",
              str(list(ref_dict_relations)),
          )
  
      if RelationType.NO_RELATION_FOUND in vo_relations:
          logger.info("No Verb-Ocean relation found")
      else:
          logger.info(
              "Found Verb-Ocean relations-%s", str(list(vo_relations))
          )
  
      if RelationType.NO_RELATION_FOUND in wiki_relations:
          logger.info("No Wikipedia relation found")
      else:
          logger.info(
              "Found Wikipedia relations-%s", str(wiki_relations)
          )
      if RelationType.NO_RELATION_FOUND in embed_relations:
          logger.info("No Embedded relation found")
      else:
          logger.info(
              "Found Embedded relations-%s", str(list(embed_relations))
          )
      if RelationType.NO_RELATION_FOUND in wn_relaions:
          logger.info("No Wordnet relation found")
      else:
          logger.info("Found Wordnet relations-%s", str(wn_relaions))
  
file_path: nlp-architect/examples/cross_doc_coref/relation_extraction_example.py
imports: import logging
  from nlp_architect import LIBRARY_ROOT
  from nlp_architect.common.cdc.mention_data import MentionDataLight
  from nlp_architect.data.cdc_resources.relations.computed_relation_extraction import ComputedRelationExtraction
  from nlp_architect.data.cdc_resources.relations.referent_dict_relation_extraction import ReferentDictRelationExtraction
  from nlp_architect.data.cdc_resources.relations.relation_types_enums import RelationType, EmbeddingMethod
  from nlp_architect.data.cdc_resources.relations.verbocean_relation_extraction import VerboceanRelationExtraction
  from nlp_architect.data.cdc_resources.relations.wikipedia_relation_extraction import WikipediaRelationExtraction
  from nlp_architect.data.cdc_resources.relations.word_embedding_relation_extraction import WordEmbeddingRelationExtraction
  from nlp_architect.data.cdc_resources.relations.wordnet_relation_extraction import WordnetRelationExtraction
label_desc: Use image node_id 1 for calling a global function with example usage: run_example() without return types